Active learning platforms are innovative tools in machine learning that integrate data annotation, model training, and deployment into a continuous, agile process, as opposed to the traditional linear and segmented workflow. These platforms facilitate collaboration between data scientists and domain experts by combining model training with data annotation, allowing for real-time feedback and iterative improvements in model performance. This approach minimizes the volume of data required and ensures models remain up-to-date with evolving real-world data, avoiding the need for complete redeployment. Active learning platforms provide the necessary infrastructure, enabling seamless integration of modeling and labeling processes and allowing teams to focus on domain-specific tasks without the burden of extensive infrastructure setup. Raza Habib, CEO and Cofounder of Humanloop, highlights the advantages of active learning in machine learning, citing it as a transformative approach that has been successfully implemented by advanced teams like Tesla, and he emphasizes Humanloop's efforts in building such a platform for natural language processing (NLP).